Contingency Plans Research Articles

A Sugeno ANFIS Model Based on Fuzzy Factor Analysis for IS/IT Project Portfolio Risk Prediction

Risk inherence jeopardises Information System (IS) and Information Technology (IT) Project Portfolio Management (PPM) to realise thestrategic objectives. Previous studies have mainly provided Artificial Intelligence (AI) and statistical models to predict the overall riskof IS/IT project portfolio, whereas neuro-fuzzy models were rarely used. This paper proposes a Sugeno Adaptive Neuro-Fuzzy InferenceSystem (ANFIS) model based on Fuzzy Factor Analysis (FFA) named ANFIS-OPR to predict the overall risk of IS/IT project portfolio fromhistorical IS/IT project risk data. The ANFIS-OPR inputs are the relevant factor loadings resulting from the FFA application on the IS/IT projects risks set to cope with the curse of dimensionality. Then, the Sugeno ANFIS model is adopted to give strategic interpretability to the predicted IS/IT project portfolio overall risk by implementing the IS/IT Project Management Office (PMO) expert knowledge, represented by fuzzy rules, on the relationship between IS/IT project portfolio strategic alignment and the IS/IT projects risks. The ANFISOPR outputs are the predicted Overall Portfolio Risk (OPR) and Root Mean Square Error (RMSE). The paper also presents an IS/IT PMO case study that shows the proposed ANFIS-OPR efficacy, which predicted the OPR values closely to the OPR estimates with an accepted RMSE of 0.108. The proposed ANFIS-OPR is a novel intelligent decision-making tool that enables the IS/IT PMO to monitor the OPR, considering its linkage with strategic alignment; thus, contingency plans can be carried out appropriately while ensuring that the IS/IT project portfolio is strategically aligned.

Beyond Conventional Threat Defense: Implementing Advanced Threat Modeling Techniques, Risk Modeling Frameworks and Contingency Planning in the Healthcare Sector for Enhanced Data Security

This research study explores the integration of advanced threat and risk modeling with scenario and contingency planning to enhance security and resilience within healthcare organizations. Given the escalating complexity and frequency of cyber threats targeting the healthcare sector, the study assesses the efficacy of a synergistic cybersecurity approach. Utilizing a quantitative research methodology, data were collected through a survey administered to 452 healthcare practitioners, focusing on their perceptions and experiences with cybersecurity threats, risk modeling techniques, and the effectiveness of scenario and contingency planning. The survey incorporated a structured questionnaire utilizing Likert scale closed-ended questions, analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to test the proposed four hypotheses related to the impact of advanced threat and risk modeling and scenario and contingency planning on the cybersecurity posture and operational resilience of healthcare organizations. Results indicated a significant positive relationship between advanced threat modeling and scenario planning with the cybersecurity posture of healthcare organizations, which, in turn, notably enhances organizational resilience. Specifically, the study found that integrating advanced threat and risk modeling with scenario and contingency planning significantly improves the ability of healthcare organizations to identify, assess, and mitigate cyber threats effectively. Furthermore, the findings suggest that such integration contributes to more informed decision-making, improved information security management practices, and more effective and efficient digital crime investigation processes. The research study concludes that a comprehensive approach integrating advanced threat and risk modeling with scenario and contingency planning significantly enhances healthcare organizations' cybersecurity posture and operational resilience. The study recommends adopting an integrated cybersecurity strategy, continuous cybersecurity education for all organizational members,regular cybersecurity assessments, and fostering collaboration for enhanced cybersecurity intelligence.

Multicast Radar Data Transmission Using the Tunnel in Tunnel Method and IGMP (Internet Group Management Protocol) Filtering by Utilizing the Internet Network

This research proposes the implementation of multicast radar data transmission using the tunnel in tunnel method by utilizing the installed IP VPN network. The background of this study is the failure in radar data transmission resulting in a change of service from surveillance to procedural service, as well as the impact of the Covid-19 pandemic on aviation organizations. The purpose of this implementation is to enhance flight safety while achieving cost efficiency for the company. Objectives include changing transmission methods, budget efficiency, improving operational safety, compliance with international standards, and support for the concept of flight information management. Advantages of multicast radar data transmission include centralized IP base, ease of monitoring, and the use of FO converters for surge protection. Implementation is based on aviation regulations, international standards, and partnership contracts. Contingency plans cover network and device failure scenarios. Cost benefit analysis shows significant financial gains from this implementation. It is hoped that this research will make a positive contribution to improving efficiency and operational safety in aviation.

Environmental and social impacts of carbon sequestration.

Climate change requires major mitigation efforts, mainly emission reduction. Carbon sequestration and avoided deforestation are complementary mitigation strategies that can promote nature conservation and local development but may also have undesirable impacts. We reviewed 246 articles citing impacts, risks, or concerns from carbon projects, and 78 others related to this topic. Most of the impacts cited focus on biodiversity, especially in afforestation projects, and on social effects related to avoided deforestation projects. Concerns were raised about project effectiveness, the permanence of carbon stored, and leakage. Recommendations include accounting for uncertainty, assessing both mitigation and contribution to climate change, defining permanence, creating contingency plans, promoting local projects, proposing alternative livelihoods, ensuring a fair distribution of benefits, combining timber production and carbon sequestration, ensuring sustainable development and minimizing leakage. A holistic approach that combines carbon sequestration, nature conservation, and poverty alleviation must be applied. The potential occurrence of negative impacts does not invalidate carbon projects but makes it advisable to conduct proper environmental impact assessments, considering direct and indirect impacts, minimizing the negative effects while maximizing the positive ones, and weighing the trade-offs between them to guide decision-making. Public participation and transparency are essential. Integr Environ Assess Manag 2024;20:1812-1838. © 2024 SETAC.

MATHEMATICAL MODEL OF A LARGE RESERVOIR, ESTABLISHING AND FINDING ITS MAIN ENERGY CHARACTERISTICS

Water plays an important role in the normal life of a person as a member of society. The presence or absence of fresh water reserves is one of the most important economic and social factors for the successful existence and development of any country in modern conditions. It is difficult to overestimate the importance of having large fresh water reserves for every sector of the economy. Ukraine has one of the largest such reserves in the world. Over time, as established by research and forecasts, the value and cost of fresh water will only increase. Therefore, preserving, protecting, properly utilizing and maintaining large, man-made water reservoirs is an important and always relevant task for the people of Ukraine. This strategy seems especially important in the current state of full-scale military aggression by the neighboring enemy. Thus, it seems reasonable to care about the preservation and proper use of fresh water in the context of the current geopolitical conflict. Water resources indeed play a critical role in all areas of the economy and daily life. Fresh water supplies are essential for agriculture, industry, drinking water for the population, as well as for energy and other industries. Ukraine, as a country with large freshwater reserves, has the opportunity to use this resource to ensure sustainable development and reduce dependence on imports. Water reservoirs can also play a strategic role in the face of possible threats and military conflicts. The storage and maintenance of water reservoirs is a necessary precautionary measure in case of any possible threats to infrastructure and access to fresh water. It is also important to develop and implement strategies for water conservation and efficient use to guarantee long-term resilience and sustainability in the water supply sector. The context of armed conflict can pose additional challenges to water management. It is therefore important to have contingency plans in place and to ensure the safety of water bodies in such circumstances. The development and improvement of water supply systems, as well as the implementation of effective water management strategies, will contribute to the sustainable development and conservation of an important natural resource, which is fresh water. Taking into account the sufficient importance and relevance of all these issues, in the following studies, a certain mathematical model of a large modern reservoir is proposed and investigated in the form of such a volumetric geometric structure as a spatially horizontally oriented elliptical half cylinder with the corresponding parameters that correspond to the specific dimensions of a real reservoir, such as the length, width and depth of filling such a structure with water.It is believed that the height (depth) of the filling can change, and this fact is essentially the most important factor in the corresponding change in the current volume of stored water and the potential energy of the accumulated liquid.The main results of this work are the possibility of determining the value of the current volume and the accumulated potential energy of such a reservoir. The paper presents a geometric view of the model of such a structure, formulas and their results for finding the values of the mentioned energy characteristics of a reservoir of the corresponding geometry, draws conclusions, points out the advantages and possible disadvantages of the given mathematical model, and provides opportunities for the future creation of an alternative mathematical more adequate model of a modern reservoir in the form of a horizontally oriented elliptical half cone.

A Decision Support Model for Barriers and Optimal Strategy Design in Sustainable Humanitarian Supply Chain Management

Sustainable humanitarian supply chain (SHSC) management enables effective and efficient responses to natural and human-made disasters. Existing literature falls short of offering decision support (DS) models to address the barriers and strategies to designing SHSC. To this end, this study develops a DS model that identifies and prioritizes the barriers to SHSC and determines optimal strategies for mitigating those barriers. This study adopted both qualitative and quantitative approaches. As part of the qualitative approach, a field study was applied using in-depth interviews to determine the barriers and corresponding strategies, while under the quantitative approach, a quality function deployment (QFD) integrated optimization technique was used to prioritize barriers and determine optimal strategies to mitigate the SHSC barriers. The study found that a lack of contingency planning, the prevalence of corruption and political interference, and a lack of social and environmental awareness are the most important barriers, while logistics outsourcing, supply chain (SC) performance management, and SC flexibility are the most essential strategies. We also found that our DS model is highly flexible and can be adapted under different scenarios, which makes the model applicable to different contexts. This study has a significant contribution to literature and practice. We developed a novel decision model that captured cost savings and leveraged both cost and time savings from interrelated strategies to determine the best optimal strategy while applying QFD-integrated optimization modeling. The paper’s findings will assist humanitarian SC managers in designing an effective, efficient, and sustainable humanitarian SC.

Understanding Intimate Partner Femicide and Attempted Intimate Partner Femicide During and After COVID-19: A Comprehensive Strain-Based Approach

Intimate partner femicide (IPF) and attempted intimate partner femicide (AIPF) are severe forms of violence and global phenomena occurring in all societies worldwide. However, explaining IPF and AIPF is a complex and challenging endeavor. This article delves into the evolution of IPF and AIPF in Portugal during and after COVID-19, seeking to comprehend the underlying explanatory factors through a comprehensive strain-based approach. IPF rates during the COVID-19 pandemic have remained relatively stable, comparable to those observed before the pandemic, following a pattern akin to that documented in other countries. However, a significant increase in AIPF occurred during the COVID-19 pandemic, and a slight uptick in IPF indicators was noted toward the end of the pandemic period (2022). Perpetrators faced various strains, such as separation and financial difficulties, along with experiencing emotions like anger, jealousy, anxiety, and depression, leading to instances of IPF and AIPF. The COVID-19 crisis and associated restrictions introduced additional strains, including social isolation, economic losses, anxiety, and widespread fear, which may contribute to psychological distress, stress, and depression, thereby heightening the likelihood of committing IPF and AIPF. The pandemic restrictions also resulted in limited access to support and protection services, hindering victims from seeking help. Establishing a contingency plan for use in future crises (including pandemics) is imperative to promote support for victims and prevent situations of IPF and AIPF.

Environmental risk assessment of underground concentrated brine reservoir with solute transport model: A case study of a coal mine in Northwest China

Underground reservoirs have emerged as a promising technology for storing and reusing saline wastewater from coal mines. However, the high salinity and potential leakage risks pose environmental threats needing assessment. This study developed an integrated solute transport and relative risk modeling framework to evaluate the environmental risks of an underground concentrated brine reservoir (UCBR) in Northwest China. The solute transport model simulated subsurface brine migration under long-term penetration, sudden leakage, and complete leakage scenarios, parameterized by field data. The relative risk model integrated source, receptor, and exposure analyses to quantify normalized risk levels. Results revealed that risks incrementally rose over longer storage durations, with 70 years posing high risk under 10 m hydraulic gradients in the long-term scenario. The risk of sudden leakage is controllable within 30 days but dramatically elevated by 60 days. Complete leakage was predicted to affect a stable 3 km2 zone. The integrated modeling provides an improved methodology for assessing UCBR risks and highlights the critical influence of bottom barrier permeability, groundwater gradients, and storage time. Targeted strategies are proposed, including reinforcing bottom barriers, managing hydraulic gradients, controlling storage durations, implementing early warning systems, and contingency planning to promote sustainable mining water management.

Assessing lava flow susceptibility at neighbouring volcanoes: Nyamulagira and Nyiragongo volcanoes, Virunga Volcanic Province

Assessing volcanic hazards in locations exposed to multiple central volcanoes requires to consider multiple potential eruption sources and their respective characteristics. While this is common practice in ashfall hazard assessment, this is generally not considered for topography-controlled volcanic flow processes. Yet, in volcanic areas with closely spaced volcanic systems, eruptions fed from several contrasted volcanic systems might threaten one given area. Considering the case of the Nyiragongo and Nyamulagira volcanoes in the Virunga Volcanic Province (D.R.Congo), we present a method to produce a combined lava flow inundation susceptibility map that integrates both volcanoes. The spatial distribution of the probability of vent opening for the next eruption is separately constrained for both volcanoes based on the mapping of historical and pre-historical eruptive vents and fissures. The Q-LavHa lava flow probability model is then calibrated separately for each volcano, considering several historical lava flows of Nyamulagira (2004, 2006, 2010) and Nyiragongo (2002). The maps for the two volcanoes are thereafter integrated based on a weighted sum of both individual lava flow inundation probability maps, assuming historically-based relative eruption frequency of the two volcanoes. The accuracy of this probabilistic susceptibility map for the most active volcanic region in Africa was unfortunately validated by the May 2021 lava flow produced by Nyiragongo. This map was discussed and validated in 2019 with local scientists, as well as representatives of disaster management and urban planning institutions, but was not included in the regional contingency plan ahead of the 2021 eruption crisis. Updating the volcanic crisis and evacuation management plans with this lava flow probability map could contribute to reinforce risk awareness among the population and inform the future development of the city of Goma.

Three Lessons From the 2004 “Big Bang” Enlargement

The 2004 “Big Bang” enlargement was a powerful reminder that European integration is an instrument for peace and not just prosperity. The pace of that enlargement depended more on the requirements for stability than on the transformation of the candidate countries. It was also a reminder of the importance of forward-looking analysis. Candidates might meet the criteria for membership at the time of accession, but that is no guarantee that they will develop in ways that continue to reflect those criteria once they have gained entry. Finally, it was a reminder that enlargement changes the experience of membership for all member states and not just for those countries that gain entry. A larger Union requires greater self-discipline to hold down congestion in decision-making and greater multilateral surveillance to prevent the actions of one member state from undermining the benefits of membership for the rest. These reminders are important lessons in planning the European Union’s next historic enlargement. The next enlargement will follow a pace set by security considerations more than the transformative power of the accession process. It will depend on a robust analysis of convergence together with contingency planning for any staged accession. And it will require commitment from existing member states as well as candidate countries to what will become a very different European Union. This next enlargement will be challenging for all parts of Europe. Nevertheless, it is better than the alternative of no enlargement or an accession process with no credible endgame.

Collaborative Governance dalam Kesiapsiagaan Bencana Gunung Sinabung di Kabupaten Karo Provinsi Sumatera Utara

This article aims to explain collaborative governance in Mount Sinabung disaster preparedness, focusing on the collaborative process. The problem centers on the Disaster Resilient Village which was formed by the Karo Regency BPBD, but during its establishment from 2019 to date, it has not implemented the Destana program in disaster preparedness. To answer this problem, the Collaborative Governance theory of Chris Ansell and Alison Gash is used. Data were collected through interviews and triangulation, as well as a literature review, and analyzed qualitatively. This study concludes the collaborative process of the Runggu of the Karo District Government on disaster management and the Runggu tradition in the village. Building trust by forming Destana and the Destana program that is included in the village budget. Commit to a joint agreement to prepare the documents for the Village Disaster Management Plan and Village Contingency Plan. Then the document is understood and implemented together according to their respective roles. This resulted in medium-term results in Gung Pinto Village receiving a Village Resilience rating in the Primary category. The findings from this study were that of the 14 villages formed, only 1 village could carry out the collaboration process well. With recommendations for the need for an empowerment process in collaborative governance in disaster preparedness at Mount Sinabung in Karo Regency, North Sumatra Province.

Feature extraction for artificial intelligence enabled food supply chain failure mode prediction

The Farm to Fork Strategy of the European Commission is a contingency plan aimed at always ensuring a sufficient and varied supply of safe, nutritious, affordable, and sustainable food to citizens. The learning from previous crises such as COVID-19 indicates that proactive strategies need to span numerous levels both within and external to food networks, requiring both vertical and horizontal collaborations. However, there is a lack of systematic performance management techniques for ripple effects in food supply chains that would enable the prediction of failure modes. Supervised learning algorithms are commonly used for prediction (classification) problems, but machine learning struggles with large data sets and complex phenomena. Consequently, this research proposes a manual approach to feature extraction for artificial intelligence with the aim of reducing dimensionality for more efficient algorithm performance, and improved interpretability/explainability for benefits in terms of ethics and managerial decision-making. The approach is based on qualitative comparative analysis informed by in-depth case knowledge which is refined through Boolean logic, yielding solutions that reflect complex causality as opposed to single failure point modes. Two case exemplars are presented to support the proposed framework for implementation: export readiness of dairy supply chains under the Russia-Ukraine war, and egg supply chain sustainability during COVID-19 lockdown in the United Kingdom.

Supply Chain Resilience and Firm Performance: A Comparative Analysis of Manufacturing Companies in Switzerland

Purpose: The aim of the study was to analyze the supply chain resilience and firm performance: a comparative analysis of manufacturing companies in Switzerland. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: The study underscores the importance of proactive measures like contingency planning and supplier diversification. It also emphasizes the need for continual adaptation of resilience strategies to changing market conditions. Overall, the research highlights the critical role of supply chain resilience in enhancing firm performance and competitiveness in Switzerland's business landscape. Unique Contribution to Theory, Practice and Policy: Resource-based view (RBV), institutional theory & transaction cost economics (TCE) may be used to anchor future studies on analyze the supply chain resilience and firm performance: a comparative analysis of manufacturing companies in Switzerland. Spanish manufacturing companies should prioritize investments in technological capabilities, such as advanced analytics and real-time monitoring systems, to enhance their ability to detect and respond to disruptions in the supply chain promptly. Policymakers should focus on creating an enabling environment for innovation and digitalization within the manufacturing sector through incentives, subsidies, and supportive regulatory frameworks.

Establishing a plan to improve pediatric patient comfort during PIV insertions and blood specimen collection: a quality improvement effort.

Patient comfort during peripheral intravenous (PIV) insertion and specimen collection was increased. The authors extended the contingency plan implemented for PICC insertion to include PIV insertion and specimen collection. The authors met their goals by using quality improvement methodology. Prioritizing patient comfort often requires institutional culture change. Needle procedures can cause pain and distress, especially in pediatric patients.1 Retrospective data collected at a freestanding pediatric facility revealed that approximately 30% of pediatric patients were not demonstrating sufficient levels of comfort during peripheral intravenous (PIV) catheter insertion and specimen collection (lab draws) even after successful implementation of comfort measures by the vascular access team (VAT) in an adjacent procedure (eg peripherally inserted central catheter placement). The current quality improvement project was implemented to support adaptation and expansion of previous lessons learned to PIVs and lab draws specifically. The VAT used the Pediatric Sedation State Scale,2 a standardized assessment tool integrated into the electronic medical record, to assess procedural comfort during PIVs and lab draws from February 2021 through April 2023. A total of 24 134 patients aged 0 to 18 years were included in the data collection. Interventions were delivered concurrently and included (1) reeducation/ongoing support for implementation of the Comfort Promise3 measures, (2) the creation and implementation of advanced comfort options, and (3) culture change. The goal of the interventions was to improve the percentage of pediatric patients achieving adequate levels of comfort beginning at 68% in year 1 to 90% in year 2. From February 2021 to April 2023, the VAT team was able to improve procedural comfort scores from 68% to 90% of pediatric patients with adequate comfort for lab draws and/or PIV insertions. While standard comfort measures are a good first step in pain management during needle procedures, they are not sufficient for every pediatric patient. Nitrous, sedation, and the use of anxiolytics and analgesics can play an important role in reducing pain and anxiety during needle procedures and should be considered for patients not achieving adequate levels of comfort with standard comfort measures.

Economic Impacts of the 2019 Drought Contingency Plan in the Lower Colorado River Basin: Water, Energy, and Recreation

Economic Impacts of the 2019 Drought Contingency Plan in the Lower Colorado River Basin: Water, Energy, and Recreation

Further Evidence for Breeding White-Beaked Dolphin (Lagenorhynchus albirostris) in Inner Danish Waters

The white-beaked dolphin (Lagenorhynchus albirostris) is the second most frequently stranded cetacean species along the Danish coastline. The northern North Sea, the Skagerrak, the Kattegat and the Danish straits are part of the species distributional range. Here, we present eight incidents of breeding activity for the white-beaked dolphin in the inner Danish waters, reviewed from yearly reports made by the National Contingency Plan concerning strandings of marine mammals in Denmark from 2009 to 2023, Danish Wildlife Diseases Surveillance reports from 2014 to 2023 and the citizen science database Naturbasen in the period 2002 to 2023. Three pregnant females, three lactating females and one calf were found stranded in the inner Danish waters. Besides this, there have been live sightings of a female with a newborn calf. We conclude that the white-beaked dolphin is breeding in the inner Danish waters.

The economics of deep and machine learning-based algorithms for COVID-19 prediction, detection, and diagnosis shaping the organizational management of hospitals

Research background: Deep and machine learning-based algorithms can assist in COVID-19 image-based medical diagnosis and symptom tracing, optimize intensive care unit admission, and use clinical data to determine patient prioritization and mortality risk, being pivotal in qualitative care provision, reducing medical errors, and increasing patient survival rates, thus diminishing the massive healthcare system burden in relation to severe COVID-19 inpatient stay duration, while increasing operational costs throughout the organizational management of hospitals. Data-driven financial and scenario-based contingency planning, predictive modelling tools, and risk pooling mechanisms should be deployed for additional medical equipment and unforeseen healthcare demand expenses. Purpose of the article: We show that deep and machine learning-based and clinical decision making systems can optimize patient survival likelihood and treatment outcomes with regard to susceptible, infected, and recovered individuals, performing accurate analyses by data modeling based on vital and clinical signs, surveillance data, and infection-related biomarkers, and furthering hospital facility optimization in terms of intensive care unit bed allocation. Methods: The review software systems employed for article screening and quality evaluation were: AMSTAR, AXIS, DistillerSR, Eppi-Reviewer, MMAT, PICO Portal, Rayyan, ROBIS, and SRDR. Findings & value added: Deep and machine learning-based clinical decision support tools can forecast COVID-19 spread, confirmed cases, and infection and mortality rates for data-driven appropriate treatment and resource allocations in effective therapeutic and diagnosis protocol development, by determining suitable measures and regulations and by using symptoms and comorbidities, vital signs, clinical and laboratory data and medical records across intensive care units, impacting the healthcare financing infrastructure. As a result of heightened use of personal protective equipment, hospital pharmacy and medication, outpatient treatment, and medical supplies, revenue loss and financial vulnerability occur, also due to expenses related to hiring additional staff and to critical resource expenditures. Hospital costs for COVID-19 medical care, screening, treatment capacity expansion, and personal protective equipment can lead to further financial losses while affecting COVID-19 frontline hospital workers and patients.

Modelling fuel oil transformation on geographically different seacoasts and assessing their self-cleansing capacity.

The present paper considers the results of long-term (up to 17years) in situ and laboratory research carried out on oiled French, Spanish, and Russian seacoasts. The objective of this research is to quantify the influence of geographical factors on the rates of natural transformation of the heavy fuel oil stranded ashore and to develop an empirical statistical model in order to evaluate the self-cleansing capacity of the coastal environment. In a number of field campaigns, 363 samples of weathered oil slicks and tar balls have been collected and analysed with the use of thin-layer chromatography combined with optical and gravimetric methods. The results obtained have been subjected to multiple nonlinear regression analyses. It has been shown that heavy fuel oil natural attenuation is more active in continental or estuarine environments influenced by nutrient-rich freshwater runoff and characterised by a higher number of sunny days, solar irradiation, and large temperature fluctuations. On the oceanic coasts, especially in sectors with low hydrodynamic energy, these processes take more time. The resulting model allows for the identification and mapping of the most vulnerable seacoasts, characterised by a low potential to degrade oil pollution. This information may be used in the contingency plans in order to optimise clean-up techniques and associated costs.

Resilience and adaptation in food supply chains during disasters: A comprehensive literature review

Resilience and adaptation in food supply chains are critical considerations in ensuring food security during times of disaster. This review investigates the challenges and opportunities that arise in the face of natural and man-made disasters by examining their impacts on the stability and functionality of food supply chains. The purpose of our research is to facilitate the development of more sustainable and resilient food supply chains that can navigate through the challenges posed by the COVID-19 pandemic and other potential disasters in the future. By analyzing case studies and empirical data, the review highlights the importance of proactive measures in building a resilient supply chains and adaptability that increases the export volumes to raise national economy wealth in profit motive as well as the implementation of robust contingency plans, the development of adaptive strategies and the integration of technological innovations can boost a food supply chain. Furthermore, it explores the role of stakeholders, policymakers and international organizations in fostering a collaborative framework for mitigating risks and enhancing the capacity of food systems to withstand and recover from adverse events. The findings underscore the need for a comprehensive approach that prioritizes risk management, fosters sustainable practices and promotes inclusive decision-making to ensure the long-term resilience of food supply chains in the face of diverse and evolving challenges.

Big data-driven risk decision-making and safety management in agricultural supply chains

In the era of digitization, the integration of big data technologies has become instrumental in advancing agricultural supply chain management and bolstering risk decision-making processes. Agricultural supply chains, critical to ensuring food security and bolstering rural economies, face vulnerabilities stemming from a myriad of internal and external elements, including natural disasters and market dynamics. Consequently, the urgency to adopt effective risk management strategies is paramount. Contemporary studies have explored the utilization of big data in decision-making processes specific to agricultural supply chain risks, predominantly concentrating on preliminary risk prediction and characterization. Nonetheless, there exists a shortfall in comprehensively analyzing the intricate interplay among risk factors and establishing a holistic risk management decision-making framework based on such analyses. This research addresses these deficiencies through two principal investigative components. First, this research explores the analysis of risk factors and their interrelationships in the agricultural supply chain based on a decision tree algorithm with a transition structure. This algorithm enhances decision-makers’ understanding of risk factors and their interrelationships, and guide the implementation of effective risk mitigation measures and the formulation of contingency plans. Subsequently, the research constructs a corresponding data-driven multi-criteria decision-making method, assisting managers in balancing different risk management strategies in a volatile supply chain environment, considering costs, benefits, and feasibility to formulate the optimal strategy. The innovation of this research lies in the development of a novel risk analysis tool based on the transition decision tree algorithm. This is the first time that such advanced algorithms are applied to agricultural supply chain risk management, filling a gap in the current research. The outcomes of this study not only contribute to enhancing risk management practices within agricultural supply chains but also offer novel insights and methodological tools that are applicable in research and practices across related domains.